Abstract P1-09-06: Breast cancer in the Pan-American region: Inequities in incidence and mortality rates according to the human development index
Bibliographic record
Abstract
Abstract Background: Breast cancer (BC) is considered a public health problem in countries of the Pan-American Health Organization (PAHO). Data from GLOBOCAN 2008 shows BC age-adjusted incidence and mortality rates of 57.1 and 13.7 per 100,000 inhabitants in the region. The aim of the present study is to evaluate the association between BC incidence and mortality rates with the human development index (HDI) in PAHO countries. Methods: This is an ecological analysis including 29 countries (PAHO) with reported data both in GLOBOCAN 2008 and in the 2013 United Nation Development Reports (UNDP). In alphabetical order the participant countries were Argentina, Bahamas, Barbados, Belize, Bolivia, Brazil, Canada, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Suriname, Trinidad and Tobago, United States of America, Uruguay and Venezuela. HDI is a composite statistic of life expectancy, education, and income and was analyzed as a continuous score. Age-adjusted BC incidence and mortality rates were taken from GLOBOCAN 2008 and log-transformed due to skewness. Pearson correlation and simple linear regression were performed using Stata 12 (Stata Corp., College Station, USA). Results: A positive correlation was found between HDI and log-transformed age-adjusted BC incidence and mortality rates. The correlation coefficient between HDI and BC incidence rate was 0.68 (p-value<0.001). The correlation with BC mortality rate was 0.49 (p-value = 0.007). Linear regression showed that an increase in one HDI unit lead to a gain of 3.51 points (se = 0.72; p-value<0.001) in the incidence rate and 2.14 points (se = 0.73; p<0.007) in the mortality rate. Conclusion: HDI inequities are important and should be considered in the analysis of the difference in BC incidence and mortality rates seen in PAHO countries. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-09-06.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".